Hypothesis testing refers to the formal procedures used by statisticians to reject or fail to reject the statistical hypotheses (LaMorte, 2017). The best way to determine whether a statistical hypothesis is true would be to examine the entire population (LaMorte, 2017). Since that is often impractical, researchers typically examine a random sample from the population (LaMorte, 2017). Random samples are the best method for selecting a sample from the population of interest (Greenwood & Banner, 2015). With a random sample, everyone within a population has an equal chance of being selected (McLeod, 2014).
The random sample, the participants in the study, should be as representative, as possible, of the population (McLeod, 2014). The more representative the sample, the more confident the researcher can be that the results, can be generalized to the population (McLeod, 2014). However, when sampling a population, if the population is very large, then a large sample is needed to be representative (McLeod, 2014). Likewise, if the population
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In other words, the sample is an accurate reflection of the population from which the sample is drawn (LaMorte, 2017). What impact does a representative sample have on research (LaMorte, 2017)? If the representative sample is not consistent with the statistical hypothesis, the hypothesis is rejected (LaMorte, 2017). Some researchers say that a hypothesis test can have one of two outcomes: reject or fail to reject the null hypothesis (LaMorte, 2017). Failure to reject implies that the data are not sufficiently persuasive and prefer the alternative hypothesis over the null hypothesis (LaMorte, 2017). The null hypothesis is the hypothesis that sample observations result purely from chance (LaMorte, 2017). The alternative hypothesis is the hypothesis that sample observations are influenced by some non-random cause (LaMorte,